KaydedilenlerDerginizi ekleyin

Dergiler / Marine Science and Technology Bulletin / 2026 / Volume: 8 Issue: 1

An Empirical Study of R Applications for Data Analysis in Marine Geology

Polina Lemenkova0000-0002-5759-1089
Sayfa
—
Yayın Tarihi
2019-06-30
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The innovativeness of the work consists in the multi–disciplinary approach combining GIS analysis and statistical methods of R which contributes towards studies of ocean trenches, aimed at geospatial analysis of big data.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The study focuses on the application of R programming language towards marine geological research with a case study of Mariana Trench.
📊
Temel Bulgular & Veri:Methods includes using following R libraries: {ggplot2} for regression analysis, Kernel density curves, compositional charts; {ggalt} for Dumbbell charts for data comparison by tectonic plates, ranking dot plots for correlation analysis; {vcd} for mosaic plots, silhouette plots for compositional similarities among the bathymetric profiles, association plots; {car} for ANOVA.
💡
Sonuç & Katkı:The innovativeness of the work consists in the multi–disciplinary approach combining GIS analysis and statistical methods of R which contributes towards studies of ocean trenches, aimed at geospatial analysis of big data.
🔬Tür:Research Article
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:R programming · statistical analysis · Mariana Trench

Abstract

The study focuses on the application of R programming language towards marine geological research with a case study of Mariana Trench. Due to its logical and straightforward syntax, multi–functional standard libraries, R is especially attractive to the geologists for the scientific computing. Using R libraries, the unevenness of various factors affecting Mariana Trench geomorphic structure has been studied. These include sediment thickness, slope steepness, angle aspect, depth at the basement and magmatism of the nearby areas. Methods includes using following R libraries: {ggplot2} for regression analysis, Kernel density curves, compositional charts; {ggalt} for Dumbbell charts for data comparison by tectonic plates, ranking dot plots for correlation analysis; {vcd} for mosaic plots, silhouette plots for compositional similarities among the bathymetric profiles, association plots; {car} for ANOVA. Bathymetric GIS data processing was done in QGIS and LaTeX. The innovativeness of the work consists in the multi–disciplinary approach combining GIS analysis and statistical methods of R which contributes towards studies of ocean trenches, aimed at geospatial analysis of big data.

🔖

Bu Makaleye Atıf Yap

Lemenkova, P. (2019). An Empirical Study of R Applications for Data Analysis in Marine Geology. Marine Science and Technology Bulletin, Volume: 8 Issue: 1. https://doi.org/10.33714/masteb.486678

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (15)

  1. Butuzova, G. Y. (2003). Hydrothermal–Sedimentary Ore Formation in the World Ocean. Geos, Moscow, Russia. 136p.
  2. Dubinin, E. P. & Ushakov, S. A. (2001). Oceanic Rift Genesis. Geos, Moscow, Russia. 293p.
  3. Gardner, J. V., Armstrong, A. A., Calder, B. R. & Beaudoin, J. (2014). So, How Deep Is The Mariana Trench? Marine Geodesy, 37(1): 1–13.
  4. Garfunkel, Z., Anderson, C. A. & Schubert, G. (1986). Mantle Circulation and the Lateral Migration of Subducted Slabs. Journal of Geophysical Research, 91: 7205–7223.
  5. Gurevich, E. G. (1998). Metalliferous Sediments in the World Ocean. Nauchnyy Mir, Moscow, Russia. 340p.
  6. Horleston, A. C. & Helffrich, G. R. (2012). Constraining Sediment Subduction: A Converted Phase Study of the Aleutians and Marianas. Earth and Planetary Science Letters, 359–360: 141–151.
  7. Husson, L. (2012). Trench Migration and Upper Plate Strain over a Convecting Mantle. Physics of the Earth and Planetary Interiors, 212–213: 32–43.
  8. Ishibashi, J., Tsunogai, U., Toki, T., Ebina, N., Gamo, T., Sano, Y., Masuda, H. & Chiba, H. (2015). Chemical Composition of Hydrothermal Fluids in the Central and Southern Mariana Trough Backarc Basin. Deep–Sea Research Part II: Topical Studies in Oceanography, 121: 126–136.
  9. Lin, J. W. B. (2008). Qtcm 0.1.2: A Python Implementation of the Neelin–Zeng Quasi–Equilibrium Tropical Circulation Model. Geoscientific Model Development, 1: 315–344.
  10. Marta–Almeida, M., Ruiz–Villarreal, M., Otero, P., Cobas, P., Peliz, A., Nolasco, R., Cirano, M. & Pereira, J. (2011). OOF3: A Python Engine for Automating Regional and Coastal Ocean Forecasts. Environmental Modelling & Software, 26: 680–682.
  11. Oliphant, T. E. (2007). Python for Scientific Computing. Computing in Science & Engineering, 9: 10–20.
  12. R Core Team (2018). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. https://www.R–project.org
  13. Roberts, J. J., Best, B. D., Dunn, D. C., Treml, E. A. & Halpin, P. N. (2010). Marine Geo–Spatial Ecology Tools: An Integrated Framework for Ecological Geoprocessing with ArcGIS, Python, R, MATLAB, and C++. Environmental Modelling & Software, 25: 1197–1207.
  14. Uyeda, S. & Kanamori, H. (1979). Back–Arc Opening and the Mode of Subduction. Journal of Geophysical Research, 84: 2017–2037.
  15. Warner, J. C., Perlin, N. & Skyllingstad, E. D. (2008). Using the Model Coupling Toolkit to Couple Earth System Models. Environmental Modelling & Software, 23: 1240–1249.